The challenge of integrating multiple data types into a comprehensive database searching algorithm has yet to be adequately solved. Existing data fusion and database searching algorithms used in the spectroscopic community suffer from key disadvantages. Most notably, competing methods such as interactive searching are not scalable, and are at best semi-automated, requiring significant user interaction. For instance, the BioRAD KnowItAll® software claims an interactive searching approach that supports searching of up to three different types of spectral data using the search strategy most appropriate to each data type. Results are displayed in a scatter plot format, requiring visual interpretation (from a human operator) and restricting the scalability of the technique. Also, this method does not account for mixture component searches. Data Fusion Then Search (DFTS) is an automated approach that combines the data from all sources into a derived feature vector and then performs a search on that combined data. The data is typically transformed using a multivariate data reduction technique, such as Principal Component Analysis, to eliminate redundancy across data and to accentuate the meaningful features. This technique is also susceptible to poor results for mixtures, and it has limited capacity for user control of weighting factors.
Therefore, it is desirable to devise a system and method that allows users to identify unknown materials with multiple spectroscopic data and that is configurable to determine weights to be accorded to spectroscopic data from different spectroscopic data generating instruments for improved identification of unknown materials.